Jiangliu Wang (王江柳)

I am a postdoctoral researcher at CUHK T Stone Robotics Institute, where I work on building machines that understand the real world from videos with minimal supervision.

I obtained my PhD degree from The Chinese University of Hong Kong (CUHK) in 2020, where I was advised by Prof. Yun-hui Liu and funded by HKPFS. I obtained my bachelor degree from Nanjing University in 2015, where I was advised by Prof. Wei Li. During my PhD, I did an internship at Tencent AI Lab, where I had a wonderful time working with Dr. Jianbo Jiao, Dr. Linchao Bao, Dr. Wei Liu, and other labmates.

I am seeking collaborations. If you are interested in learning video representations, please feel free to email me.

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I am interested in learning representation from videos (along with audio as free resources) with minimal supervision. Recently, I also develop an interest in fundamental machine learning theory.

This work is an extension of our CVPR 2019 paper. I recommend the ablation study section. Our approach achieved decent performance after just one training epoch.

Line correspondences are mapped into vectors tangent to sphere. Neighboring vectors mapped from inliers exhibit a local trend consistency (analogous to “a school of fish”).

This work is inspired by the observation that human visual system is sensitive to video pace, e.g., slow motion, a widely used technique in film making.

Neural networks are asked to predict motion and appearance statistics, including the largest motion area, largest and smallest color diversity areas.

A 3D bio-constrained skeleton model is proposed to recover the corrupted skeletons and encode the body-level motion features into images.

Temporal encoded kinematics features are proposed for action recognition, which compute the linear velocity and orientation displacement based on human skeleton data.

This is an editor invitation paper. A brief review is presented to introduce our recent works on machine intelligence for real-world applications of robots. One technology leads to a startup company VisionNav.

A caring robot is developed to detect dangerous behavior of children in the domestic environment based on action recognition and object recognition technologies.

During my undergraduate study, I was lucky enough to work with Prof. Wei Li on a defense–intrusion interaction optimization problem. This work is published in a Tier 1 applied mathematics journal.

An optimal interception strategy of the defender is provided with interpretations of its physical meaning, which depends on relative mobility of the intruder and defender.

Reviewer of CVPR 2021, ICCV 2021, ICML 2021 Workshop on SSL, NeurIPS 2020 Workshop on SSL, ICRA 2020, IROS 2019, T-NNLS, RAM.

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